Can it approve automation steps?

Only named approvers can decide approvals, and the AI acts as the signed-in person.

Automation monitoring · Fieldproxy MCP

When an automation breaks, ask why

From Claude, ChatGPT or Copilot in Teams, on your live field service data, with a confirm step before anything changes.

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ClaudeChatGPTCopilot in Microsoft Teams

Illustration of Fieldproxy's MCP tools inside Claude, ChatGPT and Copilot. Names and data are examples.

Short answer

Automation monitoring from ChatGPT, Claude or Copilot: how does it work?

Fieldproxy's built-in why_did_this_automation_fail workflow lets Claude, ChatGPT or Copilot find failed runs, explain the cause, and then retry or dismiss the run, simulate a fix, or restore an earlier version of the rule. Runs and retries are proposals you confirm.

Last verified September 27, 2026

Real requests

What people ask, and the tools it calls

You ask	Fieldproxy tools	You get
“Why did last night's invoice automation fail?”	why_did_this_automation_fail (workflow), list_failed_runs	The failed runs and the cause
“Retry the three failed runs”	retry_automation_run	Retries, run on confirm
“Go back to yesterday's version of the rule”	list_automation_versions, restore_automation_version	The earlier version restored
“Would this change have worked?”	simulate_automation	A simulated run

The part nobody else does

Your AI can change the software, not just the data

Every other field service connector we checked (September 27, 2026) reads records and, at most, edits a few of them. Fieldproxy's MCP server can also build: new fields, screens, tables, automations and agents, made in a sandbox copy of your workspace and live only when you approve.

Add a field

"Add a warranty expiry date to every job." The column, the form field and the list column, in one ask.

Build a screen

"Build a subcontractor compliance screen that flags missing insurance." A new screen on your real data.

Wire an automation

"Email the service manager 30 days before a warranty runs out." Saved switched off until you turn it on.

Create an agent

Voice and text agents: validate, simulate against test conversations, then enable.

Add your own tools

Saved SQL tools such as "a customer's overdue invoices", which every AI connection in the workspace can then call by name.

Roll back anything

Every build is checked against your database before it saves, promoted from the sandbox when you approve, and can be rolled back.

Guardrails

Your AI proposes. A person confirms.

Built for operations with branches, teams and an IT review, not a single login.

Nothing changes on one call

Every write, send or action is a proposal the user confirms within 30 minutes. Writes can be undone. More than 5 rows needs bulk permission, and more than 1,000 is refused.

Keys scoped like API keys

Limit a key to certain tables, certain rows ("only the North region's jobs"), hidden columns, insert/update/delete, and an expiry from 1 hour to 90 days. Postgres enforces the row limits.

Builds save as drafts

Apps and automations are checked against the database before they save, new automations and agents are saved switched off, and public apps are never changed.

Sends are limited

Email goes only from the user's own mailbox, at most 20 sends an hour per key, inside the workspace's quiet hours. A sandbox never sends.

Everything is logged

Every call is written to an audit log with the tool, key, person and outcome. Administrators see every key and connected app, and revoking one applies on the next request.

FAQ

Questions

Can it approve automation steps?: Only named approvers can decide approvals, and the AI acts as the signed-in person.

Automation monitoring, from the chat

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